Landes, Jürgen, Williamson, Jon (2016) Objective Bayesian nets from consistent datasets. In: BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 35TH INTERNATIONAL WORKSHOP ON BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. 1757. 020007. AIP ISBN 978-0-7354-1415-0. (doi:10.1063/1.4959048)
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| Official URL http://doi.org/10.1063/1.4959048 |
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Abstract
This paper addresses the problem of finding a Bayesian net representation of the probability function that agrees with the distributions of multiple consistent datasets and otherwise has maximum entropy. We give a general algorithm which is significantly more efficient than the standard brute-force approach. Furthermore, we show that in a wide range of cases such a Bayesian net can be obtained without solving any optimisation problem.
| Item Type: | Conference or workshop item (Paper) |
|---|---|
| DOI/Identification number: | 10.1063/1.4959048 |
| Subjects: |
B Philosophy. Psychology. Religion > BC Logic Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities |
| Divisions: | Faculties > Humanities > School of European Culture and Languages > Philosophy |
| Depositing User: | Jon Williamson |
| Date Deposited: | 10 Aug 2016 08:42 UTC |
| Last Modified: | 01 Aug 2019 10:40 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/56779 (The current URI for this page, for reference purposes) |
| Williamson, Jon: |  https://orcid.org/0000-0003-0514-4209 |
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